import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
from tensorflow import keras


def preprocess(x, y):
    """
    x is a simple image, not a batch
    """
    x = tf.cast(x, dtype=tf.float32) / 255.
    x = tf.reshape(x, [28 * 28])
    y = tf.cast(y, dtype=tf.int32)
    y = tf.one_hot(y, depth=10)
    return x, y


batch_size = 128
(x, y), (x_val, y_val) = datasets.mnist.load_data()
print('datasets:', x.shape, y.shape, x.min(), x.max())

db = tf.data.Dataset.from_tensor_slices((x, y))
db = db.map(preprocess).shuffle(60000).batch(batch_size).repeat()
ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batch_size)


class CustomDense(layers.Layer):

    def __init__(self, inp_dim, outp_dim):
        super(CustomDense, self).__init__()

        self.kernel = self.add_weight('w', [inp_dim, outp_dim])
        self.bias = self.add_weight('b', [outp_dim])

    def call(self, inputs, training=None):
        out = inputs @ self.kernel + self.bias

        return out


class CustomerModel(keras.Model):

    def __init__(self):
        super(CustomerModel, self).__init__()

        self.fc1 = CustomDense(28 * 28, 256)
        self.fc2 = CustomDense(256, 128)
        self.fc3 = CustomDense(128, 64)
        self.fc4 = CustomDense(64, 32)
        self.fc5 = CustomDense(32, 10)

    def call(self, inputs, training=None):
        x = self.fc1(inputs)
        x = tf.nn.relu(x)
        x = self.fc2(x)
        x = tf.nn.relu(x)
        x = self.fc3(x)
        x = tf.nn.relu(x)
        x = self.fc4(x)
        x = tf.nn.relu(x)
        x = self.fc5(x)

        return x


network = CustomerModel()

network.compile(optimizer=optimizers.Adam(lr=0.01),
                loss=tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy']
                )

network.fit(db,
            epochs=5,
            validation_data=ds_val,
            validation_freq=2,
            validation_steps=2,
            steps_per_epoch=x.shape[0] / batch_size + 1)

network.evaluate(ds_val)

sample = next(iter(ds_val))
x = sample[0]
y = sample[1]  # one-hot
pred = network.predict(x)  # [b, 10]
# convert back to number
y = tf.argmax(y, axis=1)
pred = tf.argmax(pred, axis=1)

print(pred)
print(y)
print(tf.reduce_sum(tf.cast(tf.equal(pred, y), tf.int32)))
